# https://tensorflow.google.cn/tutorials/quickstart/advanced?hl=zh-CN
# 针对专业人员的 TensorFlow 2.0 入门
# 没有运行成功


import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Flatten, Conv2D

# 加载并准备 MNIST 数据集。
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# 使用 tf.data 来将数据集切分为 batch 以及混淆数据集：
train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)


# 使用 Keras 模型子类化（model subclassing） API 构建 tf.keras 模型
class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)


model = MyModel()

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()

optimizer = tf.keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')


@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

    @tf.function
    def test_step(images, labels):
        predictions = model(images)
        t_loss = loss_object(labels, predictions)

        test_loss(t_loss)
        test_accuracy(labels, predictions)

        EPOCHS = 5

        for epoch in range(EPOCHS):
            # 在下一个epoch开始时，重置评估指标
            train_loss.reset_states()
            train_accuracy.reset_states()
            test_loss.reset_states()
            test_accuracy.reset_states()

            for images, labels in train_ds:
                train_step(images, labels)

            for test_images, test_labels in test_ds:
                test_step(test_images, test_labels)

            template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
            print(template.format(epoch + 1,
                                  train_loss.result(),
                                  train_accuracy.result() * 100,
                                  test_loss.result(),
                                  test_accuracy.result() * 100))
